Apate.ai says thousands of AI agents posed as scam victims, logged 2.5 million conversations
Apate.ai deployed thousands of AI agents that it says posed as scam victims to engage fraudsters. The company told TechCrunch its agents have carried out 2.5 million conversations and that those interactions 'wasted 17 years of scammers’ time,' while banks and other customers are using the intelligence to detect and block scams.
AI-assisted synthesis · Based on 2 outlets' reporting · Full text read from 1 of 2 outletsHow it's made
What happened
Key facts
- Apate.ai says it deployed thousands of AI agents that posed as scam victims to engage fraudsters (per TechCrunch, mezha.net).
- Apate.ai says its agents mimic ages, genders, accents and languages to keep scammers engaged (per TechCrunch).
- Apate.ai says those interactions 'wasted 17 years of scammers’ time.' (per TechCrunch).
- Apate.ai was founded in 2024 based on research at Macquarie University in Sydney (per TechCrunch).
- Apate.ai says banks, telecoms, government agencies and utilities are using its agents' intelligence to detect fraud and inform defenses (per TechCrunch).
- Apate.ai says it has a 20-person team and has raised just under $10 million in total (per TechCrunch).
- Apate.ai says it plans to spend its latest funding round on product development and international expansion, including opening an office in London and building a larger presence in the U.S (per TechCrunch).
- Apate.ai says clients have redirected calls and used outbound agents to disrupt scam campaigns, and that one client, Commonwealth, has seen a 76% drop in publicly reported scam losses (per TechCrunch).
The brief
Apate.ai says it has deployed thousands of AI agents to pose as scam victims and that those agents have carried out 2.5 million conversations, a company spokesperson told TechCrunch. The startup presented the numbers while participating at TechCrunch Disrupt and said the interactions have collectively 'wasted 17 years of scammers’ time.'
According to TechCrunch, Apate.ai was founded in 2024 out of research at Macquarie University in Sydney and now operates a fleet of simulated victims that mimic ages, genders, accents and languages to keep fraudsters engaged. The company described the agents as an intelligence-gathering tool that records scam tactics and conversation patterns for its clients. TechCrunch reported that banks, telecoms, government agencies and utilities are among the organisations using Apate.ai’s intelligence to detect fraud and inform defensive measures. The article quotes the company saying client organisations have routed calls to Apate’s agents; one example the company gave was that TPG has diverted nearly 590,000 calls to Apate’s agents. Apate also told TechCrunch that an unnamed Australian bank used outbound agents to disrupt 22 scam campaigns and that a client named Commonwealth has seen a 76% drop in publicly reported scam losses. The TechCrunch story placed Apate.ai’s claims alongside statistics about AI-enabled crime: it noted the FBI received more than 22,000 complaints of AI-related crimes in 2025 that resulted in about $893 million in losses, with investment scams accounting for more than $632 million of that figure, according to the article. Apate.ai’s pitch to investors and potential customers, the article said, is that its agents can both consume suspicious inbound contact and proactively disrupt fraud operations. TechCrunch reported that Apate.ai employs about 20 people and has raised just under $10 million to date, and that the company plans to spend its latest round on product work and international expansion, specifically opening an office in London and growing its U.S. presence. Apate presented its technology and metrics to venture-capital judges at Disrupt, the article says. Mezha.net published a headline reporting the same deployment of AI agents to pose as scam victims and gather intelligence, corroborating that at least one other outlet covered Apate.ai’s claims. Beyond Apate.ai’s self-reported figures and client examples cited by TechCrunch, independent verification of the 2.5 million-conversation total and the claimed aggregate time wasted for scammers was not provided in the article.
Why it matters
- Individual customers of banks, telecoms and utilities face a lower incidence of successful scams if client-reported reductions in losses hold true; TechCrunch cites Commonwealth as reporting a 76% drop in publicly reported scam losses.
- Organisations that route suspicious calls to Apate.ai — for example, TPG, which Apate says diverted nearly 590,000 calls — gain recorded intelligence on scam techniques that they can use to update fraud detection systems, per Apate’s account to TechCrunch.
- Apate.ai’s fundraising and expansion plans — the company says it has raised just under $10 million and intends to open a London office and expand in the U.S. — indicate commercial demand among institutions seeking to defend customers from AI-enabled scams.
What to watch next
- Whether Apate.ai opens an office in London as the company told TechCrunch it plans to do.
- Whether Apate.ai secures a larger presence in the U.S. as the company said it intends to pursue.
- Whether client organisations publicly confirm or publish independent verification of the 2.5 million-conversation figure and the '17 years' metric Apate.ai provided to TechCrunch.
How it's being covered
Coverage analysisShow details2 source articles · 2 unique outletsCoverage mix: Too few rated· 2 CenterWhere the outlets are based: US/Canada 50% · Europe 50%
- Coverage is currently limited to 2 outlets.
How outlets' accounts compare
How we got here
Apate.ai’s founding in 2024, based on research at Macquarie University in Sydney, is the immediate backdrop to today’s report. That work built on a longer research trajectory in conversational systems that dates to Joseph Weizenbaum’s ELIZA program in 1966.
The modern technical leap that enabled large-scale, natural-sounding agents traces to the Transformer architecture introduced in 2017 by Vaswani et al., which underpins many post-2017 generative models.
Original sources
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